Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches

摘要

Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies. Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasis-associated gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant positive correlations with MACC1 in more than half of the cancer types included in the big data analysis. However, ten other genes also reported together with MACC1 in the literature showed significant positive correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306 positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched, but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while some of the results of the big data analysis were in line with the reported experimental results, hypotheses on new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation. Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental studies and testing of combination therapies.